ArticleNature communications2023
Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 55 citations in OpenAlex.
- Nanocarrier-Based Gene Delivery Systems: Mechanisms, Clinical Translation, and Future Perspectives.Biotechnology and bioengineering · 2026Review
- Towards efficient perturbation for the noncoding genome.Nature communications · 2026Article
- Genome-Wide CRISPR Screen Identifies a microRNA Orchestrating Pleiotropic Resistance to Targeted Therapy and T Cell Immunity in Melanoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Beyond DNA editing: how Cas13 redefined programmable RNA manipulation and what still limits its therapeutic promise.Nucleic acids research · 2026Review
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- A Modular and Programmable Cas13d Platform for RNA Single Nucleotide Variant Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- simCRISPR: Modeling Experimental Complexity in Pooled CRISPR Screens.bioRxiv : the preprint server for biology · 2026Article
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Photoactivatable CRISPR/Cas13d via upconversion nanoparticles for deep tissue RNA engineering and orthopedic therapy.Nature communications · 2026Article
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Characterization of gRNA-dependent and gRNA-independent off-target binding sites of PspCas13b and RfxCas13d in mammalian cells.Nucleic acids research · 2026Article
- HMPV-TherResDB: Comprehensive human metapneumovirus (HMPV) database for sequence-structure annotations, vaccine resources, and therapeutics research.Virus research · 2026Article
- Precise progerin targeting using RfxCas13d: A therapeutic avenue for Hutchinson-Gilford progeria syndrome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- A review on the mechanism and potential diagnostic application of CRISPR/Cas13a system.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Review
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Revolutionizing CRISPR technology with artificial intelligence.Experimental & molecular medicine · 2025Review
- SELECT: high-precision genome editing strategy via integration of CRISPR-Cas and DNA damage response for cross-species applications.Nucleic acids research · 2025Article
- Enhanced RNA-targeting CRISPR-Cas technology in zebrafish.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.